Enhancing Reproducibility and Robustness in Image-Text Retrieval: A Comprehensive Review and Analysis

Abstract

Image-text retrieval systems play a pivotal role in various applications such as content-based image retrieval, multimedia analysis, and visual question answering. However, ensuring the reproducibility and robustness of these systems remains a significant challenge due to factors like dataset bias, feature representation, and model architecture. This paper provides a comprehensive review of existing methodologies, techniques, and challenges related to enhancing reproducibility and robustness in image-text retrieval. We examine key factors influencing reproducibility, such as dataset construction, evaluation metrics, and experimental protocols. Additionally, we discuss strategies for improving robustness against variations in image and text modalities, including feature extraction, fusion techniques, and adversarial robustness. Through this analysis, we aim to provide insights into current trends, identify research gaps, and propose future directions for advancing reproducibility and robustness in image-text retrieval systems.